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Article: Radiomics and artificial intelligence for precision medicine in lung cancer treatment

TitleRadiomics and artificial intelligence for precision medicine in lung cancer treatment
Authors
KeywordsArtificial intelligence
Lung cancer
Precision medicine
Radiogenomics
Radiomics
Issue Date2023
Citation
Seminars in Cancer Biology, 2023, v. 93, p. 97-113 How to Cite?
AbstractLung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.
Persistent Identifierhttp://hdl.handle.net/10722/341404
ISSN
2023 Impact Factor: 12.1
2023 SCImago Journal Rankings: 3.297
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChen, Mitchell-
dc.contributor.authorCopley, Susan J.-
dc.contributor.authorViola, Patrizia-
dc.contributor.authorLu, Haonan-
dc.contributor.authorAboagye, Eric O.-
dc.date.accessioned2024-03-13T08:42:33Z-
dc.date.available2024-03-13T08:42:33Z-
dc.date.issued2023-
dc.identifier.citationSeminars in Cancer Biology, 2023, v. 93, p. 97-113-
dc.identifier.issn1044-579X-
dc.identifier.urihttp://hdl.handle.net/10722/341404-
dc.description.abstractLung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.-
dc.languageeng-
dc.relation.ispartofSeminars in Cancer Biology-
dc.subjectArtificial intelligence-
dc.subjectLung cancer-
dc.subjectPrecision medicine-
dc.subjectRadiogenomics-
dc.subjectRadiomics-
dc.titleRadiomics and artificial intelligence for precision medicine in lung cancer treatment-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.semcancer.2023.05.004-
dc.identifier.pmid37211292-
dc.identifier.scopuseid_2-s2.0-85160030281-
dc.identifier.volume93-
dc.identifier.spage97-
dc.identifier.epage113-
dc.identifier.eissn1096-3650-
dc.identifier.isiWOS:001012848000001-

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